把分类报告的值对应相加再处对应的个数python代码
时间: 2024-02-29 21:51:47 浏览: 17
假设你已经有了分类报告的结果,可以使用以下代码将对应值相加并除以相应的个数:
```python
from sklearn.metrics import classification_report
# 假设你已经有了分类报告的结果
report = classification_report(y_true, y_pred, output_dict=True)
# 将对应值相加并除以相应的个数
precision_sum = recall_sum = f1_score_sum = 0
count = len(report) - 3 # 减去最后三行的宏平均、微平均和样本总数
for key in report:
if key not in ['macro avg', 'weighted avg', 'samples']:
precision_sum += report[key]['precision']
recall_sum += report[key]['recall']
f1_score_sum += report[key]['f1-score']
precision_avg = precision_sum / count
recall_avg = recall_sum / count
f1_score_avg = f1_score_sum / count
print("平均精度:", precision_avg)
print("平均召回率:", recall_avg)
print("平均F1值:", f1_score_avg)
```
其中 `y_true` 为真实标签,`y_pred` 为模型预测标签。运行结果会输出平均精度、平均召回率和平均 F1 值。